Neural Autoregressive Modeling of Brain Aging | JoVE Visualize

Area of Science:

Computational neuroscience
Medical imaging analysis
Artificial intelligence in healthcare

Background:

Brain aging simulation is crucial for understanding neurological disorders and personalized medicine.
Existing generative models face challenges with high-dimensional neuroimaging data and capturing subtle, subject-specific aging patterns.
Predicting future brain structure from early MRI scans offers valuable insights into individual aging trajectories.

Purpose of the Study:

To develop a novel model, NeuroAR, for high-fidelity brain aging synthesis using generative autoregressive transformers.
To accurately simulate the structural evolution of the brain over time from earlier magnetic resonance imaging (MRI) scans.
To overcome the limitations of current generative models in capturing subject-specific aging patterns and data complexity.

Main Methods:

Proposed NeuroAR, a generative autoregressive transformer model for brain aging simulation.
Synthesized future brain scans by autoregressively estimating discrete token maps from concatenated embeddings of past and future scans.
Incorporated subject's previous scan, acquisition age, and target age via cross-attention to guide the generation process.

Main Results:

NeuroAR demonstrated superior performance in image fidelity compared to state-of-the-art models like latent diffusion models (LDM) and generative adversarial networks.
Evaluations on elderly and adolescent subjects confirmed NeuroAR's ability to model subject-specific brain aging trajectories.
A pre-trained age predictor validated the consistency and realism of synthesized images with expected aging patterns.

Conclusions:

NeuroAR effectively models subject-specific brain aging trajectories with high fidelity, outperforming existing generative approaches.
The model's autoregressive transformer architecture provides a robust framework for complex neuroimaging data synthesis.
NeuroAR shows significant potential for applications in clinical neuroscience, computational modeling, and personalized health monitoring.